weight_cause_cox {cmprskcoxmsm} | R Documentation |

## Inverse probability weighted proportional cause-specific hazards model

### Description

`weight_cause_cox`

fits the marginal structural proportional cause-specific hazards model using the inverse probability treatment weights.

### Usage

```
weight_cause_cox(data=,
time, time2 = NULL,
Event.var, Event,
weight.type,
ties = NULL)
```

### Arguments

`data` |
The dataset, output of |

`time` |
See also |

`time2` |
See also |

`Event.var` |
The variable name for the event indicator which typically has at least 3 levels. |

`Event` |
Event of interest, the rest of the event are treating as competing event. |

`weight.type` |
Type of inverse probability weights. Possible values are "Unstabilized" and "Stabilized". |

`ties` |
See also |

### Details

The marginal structural cause-specific Cox model for cause j usually has the form:

```
\lambda^{a}_j (t) \equiv \lambda_{T^{a},J^{a}=j}(t) = \lambda_{0j}e^{\beta*a},
```

where `T^{a}`

, `J^{a}`

is the counterfactural survival time and cause for treatment `a (=0,1)`

, `\lambda_{0j}`

is the unspecified baseline cause-specific hazard for cause j, and `\beta`

is the treatment effect.

### Value

Returns a table containing the estimated coefficient of the treatment effect, the robust standard error of the coefficient, estimated hazard ratio and 95% CI for the hazard ratio.

### See Also

*cmprskcoxmsm*version 0.2.1 Index]